Overview

Dataset statistics

Number of variables25
Number of observations99336
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.1 MiB
Average record size in memory180.0 B

Variable types

Numeric18
Categorical7

Alerts

ingresos is highly overall correlated with egresos and 2 other fieldsHigh correlation
egresos is highly overall correlated with ingresos and 2 other fieldsHigh correlation
activos is highly overall correlated with ingresos and 2 other fieldsHigh correlation
pasivos is highly overall correlated with ingresos and 2 other fieldsHigh correlation
diasMaxMorosidad is highly overall correlated with morosoHigh correlation
numeroCuotas is highly overall correlated with montoPrestamoHigh correlation
montoPrestamo is highly overall correlated with numeroCuotasHigh correlation
asesor_encoded is highly overall correlated with zona_geograficaHigh correlation
zona_geografica is highly overall correlated with asesor_encodedHigh correlation
moroso is highly overall correlated with diasMaxMorosidadHigh correlation
zona_geografica is highly imbalanced (60.2%)Imbalance
rangoPrestamo is highly imbalanced (58.3%)Imbalance
activos is highly skewed (γ1 = 132.2635378)Skewed
pasivos is highly skewed (γ1 = 30.10261271)Skewed
paisOrigen_encoded is highly skewed (γ1 = 28.65820304)Skewed
ingresos has 5954 (6.0%) zerosZeros
egresos has 6197 (6.2%) zerosZeros
activos has 3576 (3.6%) zerosZeros
pasivos has 31151 (31.4%) zerosZeros
diasMaxMorosidad has 34101 (34.3%) zerosZeros
cantidadInversiones has 82985 (83.5%) zerosZeros
grupoEconomico_encoded has 2576 (2.6%) zerosZeros

Reproduction

Analysis started2024-03-15 01:59:38.359281
Analysis finished2024-03-15 02:01:22.376316
Duration1 minute and 44.02 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

codigoOficina
Real number (ℝ)

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.2054542
Minimum1
Maximum103
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-03-14T21:01:22.529919image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q311
95-th percentile14
Maximum103
Range102
Interquartile range (IQR)8

Descriptive statistics

Standard deviation14.850958
Coefficient of variation (CV)1.6132782
Kurtosis32.472285
Mean9.2054542
Median Absolute Deviation (MAD)4
Skewness5.5729676
Sum914433
Variance220.55096
MonotonicityNot monotonic
2024-03-14T21:01:22.728450image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
1 13592
13.7%
9 11618
11.7%
2 11035
11.1%
11 8966
9.0%
6 8626
8.7%
3 7138
7.2%
10 6863
6.9%
5 6556
6.6%
14 6204
6.2%
13 5469
5.5%
Other values (5) 13269
13.4%
ValueCountFrequency (%)
1 13592
13.7%
2 11035
11.1%
3 7138
7.2%
4 3174
 
3.2%
5 6556
6.6%
6 8626
8.7%
9 11618
11.7%
10 6863
6.9%
11 8966
9.0%
12 5468
5.5%
ValueCountFrequency (%)
103 2213
 
2.2%
16 589
 
0.6%
15 1825
 
1.8%
14 6204
6.2%
13 5469
5.5%
12 5468
5.5%
11 8966
9.0%
10 6863
6.9%
9 11618
11.7%
6 8626
8.7%

edad
Real number (ℝ)

Distinct62
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.026929
Minimum19
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-03-14T21:01:22.960453image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum19
5-th percentile29
Q138
median46
Q357
95-th percentile72
Maximum80
Range61
Interquartile range (IQR)19

Descriptive statistics

Standard deviation13.112547
Coefficient of variation (CV)0.27302489
Kurtosis-0.63736892
Mean48.026929
Median Absolute Deviation (MAD)10
Skewness0.37905251
Sum4770803
Variance171.93888
MonotonicityNot monotonic
2024-03-14T21:01:23.255451image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43 2972
 
3.0%
42 2952
 
3.0%
40 2913
 
2.9%
41 2898
 
2.9%
37 2883
 
2.9%
39 2881
 
2.9%
44 2881
 
2.9%
38 2771
 
2.8%
36 2738
 
2.8%
45 2737
 
2.8%
Other values (52) 70710
71.2%
ValueCountFrequency (%)
19 1
 
< 0.1%
20 5
 
< 0.1%
21 7
 
< 0.1%
22 94
 
0.1%
23 248
 
0.2%
24 461
 
0.5%
25 617
0.6%
26 749
0.8%
27 883
0.9%
28 1160
1.2%
ValueCountFrequency (%)
80 359
 
0.4%
79 422
0.4%
78 489
0.5%
77 606
0.6%
76 617
0.6%
75 623
0.6%
74 722
0.7%
73 803
0.8%
72 753
0.8%
71 938
0.9%

ingresos
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20530
Distinct (%)20.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2577.1428
Minimum0
Maximum289500
Zeros5954
Zeros (%)6.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-03-14T21:01:23.564448image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1800
median1400
Q32540
95-th percentile8000
Maximum289500
Range289500
Interquartile range (IQR)1740

Descriptive statistics

Standard deviation5711.2119
Coefficient of variation (CV)2.2161022
Kurtosis456.49797
Mean2577.1428
Median Absolute Deviation (MAD)738
Skewness16.070059
Sum2.5600305 × 108
Variance32617941
MonotonicityNot monotonic
2024-03-14T21:01:23.825450image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5954
 
6.0%
1200 1758
 
1.8%
1500 1686
 
1.7%
1000 1587
 
1.6%
800 1421
 
1.4%
2000 1370
 
1.4%
600 1071
 
1.1%
1800 1010
 
1.0%
900 1003
 
1.0%
500 893
 
0.9%
Other values (20520) 81583
82.1%
ValueCountFrequency (%)
0 5954
6.0%
1 358
 
0.4%
1.01 2
 
< 0.1%
1.1 1
 
< 0.1%
2 33
 
< 0.1%
10 6
 
< 0.1%
20 1
 
< 0.1%
22 1
 
< 0.1%
26.25 1
 
< 0.1%
32 1
 
< 0.1%
ValueCountFrequency (%)
289500 1
 
< 0.1%
267688.41 1
 
< 0.1%
252560 1
 
< 0.1%
240196 1
 
< 0.1%
228000 1
 
< 0.1%
205467 1
 
< 0.1%
200650 1
 
< 0.1%
200476.8 1
 
< 0.1%
200000 3
< 0.1%
190000 2
< 0.1%

egresos
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21657
Distinct (%)21.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1535.1105
Minimum0
Maximum226377.03
Zeros6197
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-03-14T21:01:24.100976image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1260
median610
Q31387
95-th percentile5590.295
Maximum226377.03
Range226377.03
Interquartile range (IQR)1127

Descriptive statistics

Standard deviation4142.8779
Coefficient of variation (CV)2.698749
Kurtosis434.0669
Mean1535.1105
Median Absolute Deviation (MAD)420
Skewness15.669683
Sum1.5249174 × 108
Variance17163437
MonotonicityNot monotonic
2024-03-14T21:01:24.334903image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6197
 
6.2%
200 1386
 
1.4%
150 1094
 
1.1%
180 875
 
0.9%
160 875
 
0.9%
300 859
 
0.9%
100 857
 
0.9%
250 856
 
0.9%
220 768
 
0.8%
140 738
 
0.7%
Other values (21647) 84831
85.4%
ValueCountFrequency (%)
0 6197
6.2%
1 24
 
< 0.1%
2 1
 
< 0.1%
5 2
 
< 0.1%
10 33
 
< 0.1%
12 3
 
< 0.1%
14 1
 
< 0.1%
15 6
 
< 0.1%
16.39 1
 
< 0.1%
18 1
 
< 0.1%
ValueCountFrequency (%)
226377.03 1
< 0.1%
176132 1
< 0.1%
171817.65 1
< 0.1%
167368 1
< 0.1%
164614.18 1
< 0.1%
153520 1
< 0.1%
139748.6 1
< 0.1%
139300 1
< 0.1%
135996.31 2
< 0.1%
134872 1
< 0.1%

activos
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct25098
Distinct (%)25.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66950.023
Minimum0
Maximum50000000
Zeros3576
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-03-14T21:01:24.821524image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2200
Q115000
median40300
Q382600
95-th percentile205000
Maximum50000000
Range50000000
Interquartile range (IQR)67600

Descriptive statistics

Standard deviation240159.07
Coefficient of variation (CV)3.5871395
Kurtosis23390.085
Mean66950.023
Median Absolute Deviation (MAD)29700
Skewness132.26354
Sum6.6505475 × 109
Variance5.7676379 × 1010
MonotonicityNot monotonic
2024-03-14T21:01:25.085658image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3576
 
3.6%
5000 1946
 
2.0%
8000 911
 
0.9%
3000 831
 
0.8%
4000 776
 
0.8%
20000 750
 
0.8%
15000 720
 
0.7%
35000 705
 
0.7%
6000 701
 
0.7%
25000 699
 
0.7%
Other values (25088) 87721
88.3%
ValueCountFrequency (%)
0 3576
3.6%
1 2
 
< 0.1%
4 1
 
< 0.1%
10 1
 
< 0.1%
20 1
 
< 0.1%
40 1
 
< 0.1%
50 1
 
< 0.1%
60 2
 
< 0.1%
100 16
 
< 0.1%
120 1
 
< 0.1%
ValueCountFrequency (%)
50000000 1
< 0.1%
30800000 1
< 0.1%
25000000 1
< 0.1%
21600000 1
< 0.1%
9435000 1
< 0.1%
8500000 1
< 0.1%
7659000 1
< 0.1%
6300000 1
< 0.1%
4170000 1
< 0.1%
3694000 1
< 0.1%

pasivos
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct40483
Distinct (%)40.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12631.794
Minimum0
Maximum4229723
Zeros31151
Zeros (%)31.4%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-03-14T21:01:25.392655image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2880
Q313695
95-th percentile54758.5
Maximum4229723
Range4229723
Interquartile range (IQR)13695

Descriptive statistics

Standard deviation33276.032
Coefficient of variation (CV)2.6343076
Kurtosis2850.5805
Mean12631.794
Median Absolute Deviation (MAD)2880
Skewness30.102613
Sum1.2547919 × 109
Variance1.1072943 × 109
MonotonicityNot monotonic
2024-03-14T21:01:25.652951image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 31151
31.4%
2000 436
 
0.4%
3000 416
 
0.4%
5000 409
 
0.4%
1000 395
 
0.4%
10000 357
 
0.4%
4000 301
 
0.3%
1500 244
 
0.2%
6000 239
 
0.2%
500 233
 
0.2%
Other values (40473) 65155
65.6%
ValueCountFrequency (%)
0 31151
31.4%
0.1 1
 
< 0.1%
1 18
 
< 0.1%
1.58 1
 
< 0.1%
2 6
 
< 0.1%
2.98 1
 
< 0.1%
2.99 1
 
< 0.1%
3 2
 
< 0.1%
3.16 1
 
< 0.1%
3.17 1
 
< 0.1%
ValueCountFrequency (%)
4229723 1
< 0.1%
1700000 1
< 0.1%
1476346.89 1
< 0.1%
1315155 1
< 0.1%
1195782 1
< 0.1%
1125565 1
< 0.1%
1107643.52 1
< 0.1%
1007414.12 1
< 0.1%
1000355.81 1
< 0.1%
896783.79 1
< 0.1%

diasMaxMorosidad
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct1413
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88.66566
Minimum0
Maximum1499
Zeros34101
Zeros (%)34.3%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-03-14T21:01:25.924685image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median13
Q350
95-th percentile569
Maximum1499
Range1499
Interquartile range (IQR)50

Descriptive statistics

Standard deviation225.70658
Coefficient of variation (CV)2.5455918
Kurtosis13.73418
Mean88.66566
Median Absolute Deviation (MAD)13
Skewness3.7097743
Sum8807692
Variance50943.459
MonotonicityNot monotonic
2024-03-14T21:01:26.195687image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 34101
34.3%
2 3183
 
3.2%
4 1848
 
1.9%
12 1754
 
1.8%
3 1537
 
1.5%
32 1315
 
1.3%
7 1279
 
1.3%
27 1238
 
1.2%
17 1216
 
1.2%
22 1188
 
1.2%
Other values (1403) 50677
51.0%
ValueCountFrequency (%)
0 34101
34.3%
1 7
 
< 0.1%
2 3183
 
3.2%
3 1537
 
1.5%
4 1848
 
1.9%
5 1154
 
1.2%
6 1049
 
1.1%
7 1279
 
1.3%
8 899
 
0.9%
9 901
 
0.9%
ValueCountFrequency (%)
1499 1
< 0.1%
1498 1
< 0.1%
1495 1
< 0.1%
1494 1
< 0.1%
1487 1
< 0.1%
1480 1
< 0.1%
1474 1
< 0.1%
1473 2
< 0.1%
1470 1
< 0.1%
1468 1
< 0.1%

cantidadPrestamos
Real number (ℝ)

Distinct79
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.364903
Minimum1
Maximum126
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-03-14T21:01:26.483680image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q34
95-th percentile10
Maximum126
Range125
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.0324812
Coefficient of variation (CV)1.1983945
Kurtosis68.049258
Mean3.364903
Median Absolute Deviation (MAD)1
Skewness5.5599278
Sum334256
Variance16.260905
MonotonicityNot monotonic
2024-03-14T21:01:26.754690image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 38136
38.4%
2 19807
19.9%
3 11996
 
12.1%
4 7947
 
8.0%
5 5385
 
5.4%
6 3760
 
3.8%
7 2734
 
2.8%
8 2031
 
2.0%
9 1627
 
1.6%
10 1224
 
1.2%
Other values (69) 4689
 
4.7%
ValueCountFrequency (%)
1 38136
38.4%
2 19807
19.9%
3 11996
 
12.1%
4 7947
 
8.0%
5 5385
 
5.4%
6 3760
 
3.8%
7 2734
 
2.8%
8 2031
 
2.0%
9 1627
 
1.6%
10 1224
 
1.2%
ValueCountFrequency (%)
126 1
 
< 0.1%
115 1
 
< 0.1%
110 1
 
< 0.1%
90 1
 
< 0.1%
84 4
< 0.1%
83 1
 
< 0.1%
82 1
 
< 0.1%
81 1
 
< 0.1%
78 1
 
< 0.1%
75 1
 
< 0.1%

cantidadInversiones
Real number (ℝ)

Distinct146
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3693324
Minimum0
Maximum408
Zeros82985
Zeros (%)83.5%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-03-14T21:01:27.058700image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile7
Maximum408
Range408
Interquartile range (IQR)0

Descriptive statistics

Standard deviation6.7808143
Coefficient of variation (CV)4.9519127
Kurtosis352.30422
Mean1.3693324
Median Absolute Deviation (MAD)0
Skewness13.680606
Sum136024
Variance45.979443
MonotonicityNot monotonic
2024-03-14T21:01:27.348696image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 82985
83.5%
1 4411
 
4.4%
2 2537
 
2.6%
3 1508
 
1.5%
4 1125
 
1.1%
5 847
 
0.9%
6 671
 
0.7%
7 535
 
0.5%
8 458
 
0.5%
9 389
 
0.4%
Other values (136) 3870
 
3.9%
ValueCountFrequency (%)
0 82985
83.5%
1 4411
 
4.4%
2 2537
 
2.6%
3 1508
 
1.5%
4 1125
 
1.1%
5 847
 
0.9%
6 671
 
0.7%
7 535
 
0.5%
8 458
 
0.5%
9 389
 
0.4%
ValueCountFrequency (%)
408 1
< 0.1%
242 1
< 0.1%
238 1
< 0.1%
232 1
< 0.1%
230 1
< 0.1%
221 1
< 0.1%
217 1
< 0.1%
203 1
< 0.1%
199 1
< 0.1%
196 1
< 0.1%

numeroCuotas
Real number (ℝ)

Distinct168
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.501782
Minimum1
Maximum192
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-03-14T21:01:27.646697image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q124
median36
Q348
95-th percentile72
Maximum192
Range191
Interquartile range (IQR)24

Descriptive statistics

Standard deviation21.292185
Coefficient of variation (CV)0.58331906
Kurtosis6.8147172
Mean36.501782
Median Absolute Deviation (MAD)12
Skewness1.6929734
Sum3625941
Variance453.35714
MonotonicityNot monotonic
2024-03-14T21:01:27.971700image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
36 30740
30.9%
24 12327
12.4%
48 10095
 
10.2%
60 6841
 
6.9%
12 6745
 
6.8%
18 5469
 
5.5%
72 4150
 
4.2%
30 2419
 
2.4%
6 1790
 
1.8%
1 1765
 
1.8%
Other values (158) 16995
17.1%
ValueCountFrequency (%)
1 1765
1.8%
2 257
 
0.3%
3 629
 
0.6%
4 359
 
0.4%
5 178
 
0.2%
6 1790
1.8%
7 131
 
0.1%
8 653
 
0.7%
9 207
 
0.2%
10 461
 
0.5%
ValueCountFrequency (%)
192 19
< 0.1%
191 3
 
< 0.1%
190 6
 
< 0.1%
189 6
 
< 0.1%
188 3
 
< 0.1%
187 2
 
< 0.1%
186 6
 
< 0.1%
185 6
 
< 0.1%
184 6
 
< 0.1%
183 8
< 0.1%

tasaInteres
Real number (ℝ)

Distinct836
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.470201
Minimum3.33
Maximum24.53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-03-14T21:01:28.350700image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum3.33
5-th percentile11.1
Q114.5
median15.5
Q318.99
95-th percentile23
Maximum24.53
Range21.2
Interquartile range (IQR)4.49

Descriptive statistics

Standard deviation3.4726728
Coefficient of variation (CV)0.21084581
Kurtosis-0.64993588
Mean16.470201
Median Absolute Deviation (MAD)3
Skewness0.21895088
Sum1636083.9
Variance12.059456
MonotonicityNot monotonic
2024-03-14T21:01:28.661699image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.5 10571
 
10.6%
18.5 7889
 
7.9%
15 7270
 
7.3%
19.49 4876
 
4.9%
17 4298
 
4.3%
15.5 4169
 
4.2%
12.5 3427
 
3.4%
14.99 3385
 
3.4%
23 3294
 
3.3%
19.5 2817
 
2.8%
Other values (826) 47340
47.7%
ValueCountFrequency (%)
3.33 7
 
< 0.1%
8.4 9
 
< 0.1%
8.5 67
0.1%
8.7 2
 
< 0.1%
8.8 2
 
< 0.1%
8.9 69
0.1%
8.95 8
 
< 0.1%
9 41
< 0.1%
9.2 2
 
< 0.1%
9.35 2
 
< 0.1%
ValueCountFrequency (%)
24.53 27
 
< 0.1%
24.5 49
 
< 0.1%
24.4 35
 
< 0.1%
24.3 2
 
< 0.1%
24 155
 
0.2%
23.8 415
 
0.4%
23.73 1
 
< 0.1%
23.5 2
 
< 0.1%
23.3 2816
2.8%
23.24 5
 
< 0.1%

montoPrestamo
Real number (ℝ)

Distinct13929
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9898.8612
Minimum100
Maximum300000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-03-14T21:01:29.047699image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile997.9225
Q13000
median6180
Q310700
95-th percentile30000
Maximum300000
Range299900
Interquartile range (IQR)7700

Descriptive statistics

Standard deviation12600.046
Coefficient of variation (CV)1.2728784
Kurtosis38.17983
Mean9898.8612
Median Absolute Deviation (MAD)3820
Skewness4.7655216
Sum9.8331328 × 108
Variance1.5876117 × 108
MonotonicityNot monotonic
2024-03-14T21:01:29.339699image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 9910
 
10.0%
5000 9005
 
9.1%
8000 5468
 
5.5%
3000 4328
 
4.4%
20000 4241
 
4.3%
15000 3681
 
3.7%
6000 3438
 
3.5%
2000 3342
 
3.4%
4000 3126
 
3.1%
1000 2934
 
3.0%
Other values (13919) 49863
50.2%
ValueCountFrequency (%)
100 6
< 0.1%
100.83 1
 
< 0.1%
101.61 1
 
< 0.1%
103.11 1
 
< 0.1%
104.81 1
 
< 0.1%
106 1
 
< 0.1%
106.62 1
 
< 0.1%
107 1
 
< 0.1%
108.01 1
 
< 0.1%
108.91 1
 
< 0.1%
ValueCountFrequency (%)
300000 1
 
< 0.1%
236551.44 1
 
< 0.1%
208689.96 1
 
< 0.1%
200000 7
< 0.1%
199603.03 1
 
< 0.1%
197409.1 1
 
< 0.1%
196082.87 1
 
< 0.1%
192496.97 1
 
< 0.1%
189278.74 1
 
< 0.1%
188031.25 1
 
< 0.1%

zona_geografica
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
83061 
3
10666 
2
 
5601
4
 
8

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters99336
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 83061
83.6%
3 10666
 
10.7%
2 5601
 
5.6%
4 8
 
< 0.1%

Length

2024-03-14T21:01:29.672699image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T21:01:29.867698image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 83061
83.6%
3 10666
 
10.7%
2 5601
 
5.6%
4 8
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 83061
83.6%
3 10666
 
10.7%
2 5601
 
5.6%
4 8
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 99336
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 83061
83.6%
3 10666
 
10.7%
2 5601
 
5.6%
4 8
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 99336
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 83061
83.6%
3 10666
 
10.7%
2 5601
 
5.6%
4 8
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 99336
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 83061
83.6%
3 10666
 
10.7%
2 5601
 
5.6%
4 8
 
< 0.1%

genero
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
55661 
2
43675 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters99336
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 55661
56.0%
2 43675
44.0%

Length

2024-03-14T21:01:30.049700image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T21:01:30.238698image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 55661
56.0%
2 43675
44.0%

Most occurring characters

ValueCountFrequency (%)
1 55661
56.0%
2 43675
44.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 99336
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 55661
56.0%
2 43675
44.0%

Most occurring scripts

ValueCountFrequency (%)
Common 99336
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 55661
56.0%
2 43675
44.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 99336
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 55661
56.0%
2 43675
44.0%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
56354 
3
27442 
2
9493 
5
 
3198
4
 
2849

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters99336
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
1 56354
56.7%
3 27442
27.6%
2 9493
 
9.6%
5 3198
 
3.2%
4 2849
 
2.9%

Length

2024-03-14T21:01:30.415701image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T21:01:30.631698image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
1 56354
56.7%
3 27442
27.6%
2 9493
 
9.6%
5 3198
 
3.2%
4 2849
 
2.9%

Most occurring characters

ValueCountFrequency (%)
1 56354
56.7%
3 27442
27.6%
2 9493
 
9.6%
5 3198
 
3.2%
4 2849
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 99336
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 56354
56.7%
3 27442
27.6%
2 9493
 
9.6%
5 3198
 
3.2%
4 2849
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Common 99336
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 56354
56.7%
3 27442
27.6%
2 9493
 
9.6%
5 3198
 
3.2%
4 2849
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 99336
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 56354
56.7%
3 27442
27.6%
2 9493
 
9.6%
5 3198
 
3.2%
4 2849
 
2.9%

instruccion
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1501268
Minimum1
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-03-14T21:01:31.010696image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile3
Maximum6
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.75424443
Coefficient of variation (CV)0.35079067
Kurtosis-0.24160068
Mean2.1501268
Median Absolute Deviation (MAD)1
Skewness0.025428762
Sum213585
Variance0.56888467
MonotonicityNot monotonic
2024-03-14T21:01:31.168697image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 44529
44.8%
3 33730
34.0%
1 20452
20.6%
4 311
 
0.3%
5 243
 
0.2%
6 71
 
0.1%
ValueCountFrequency (%)
1 20452
20.6%
2 44529
44.8%
3 33730
34.0%
4 311
 
0.3%
5 243
 
0.2%
6 71
 
0.1%
ValueCountFrequency (%)
6 71
 
0.1%
5 243
 
0.2%
4 311
 
0.3%
3 33730
34.0%
2 44529
44.8%
1 20452
20.6%

moroso
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0
71394 
1
27942 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters99336
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 71394
71.9%
1 27942
 
28.1%

Length

2024-03-14T21:01:31.339702image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T21:01:31.515705image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 71394
71.9%
1 27942
 
28.1%

Most occurring characters

ValueCountFrequency (%)
0 71394
71.9%
1 27942
 
28.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 99336
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 71394
71.9%
1 27942
 
28.1%

Most occurring scripts

ValueCountFrequency (%)
Common 99336
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 71394
71.9%
1 27942
 
28.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 99336
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 71394
71.9%
1 27942
 
28.1%

rangoPrestamo
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0
85917 
3
11106 
2
 
2313

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters99336
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row3
3rd row3
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 85917
86.5%
3 11106
 
11.2%
2 2313
 
2.3%

Length

2024-03-14T21:01:31.675697image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T21:01:31.872702image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
0 85917
86.5%
3 11106
 
11.2%
2 2313
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 85917
86.5%
3 11106
 
11.2%
2 2313
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 99336
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 85917
86.5%
3 11106
 
11.2%
2 2313
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common 99336
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 85917
86.5%
3 11106
 
11.2%
2 2313
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 99336
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 85917
86.5%
3 11106
 
11.2%
2 2313
 
2.3%

profesion_encoded
Real number (ℝ)

Distinct455
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.95999
Minimum0
Maximum454
Zeros12
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-03-14T21:01:32.074702image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20
Q188
median95
Q3155
95-th percentile343
Maximum454
Range454
Interquartile range (IQR)67

Descriptive statistics

Standard deviation93.733835
Coefficient of variation (CV)0.73252453
Kurtosis1.3210243
Mean127.95999
Median Absolute Deviation (MAD)59
Skewness1.1505914
Sum12711034
Variance8786.0319
MonotonicityNot monotonic
2024-03-14T21:01:32.312703image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
95 18834
19.0%
154 14331
14.4%
20 14302
14.4%
155 9311
9.4%
88 8904
 
9.0%
236 3516
 
3.5%
5 2126
 
2.1%
151 1879
 
1.9%
111 1569
 
1.6%
357 1353
 
1.4%
Other values (445) 23211
23.4%
ValueCountFrequency (%)
0 12
 
< 0.1%
1 501
 
0.5%
2 2
 
< 0.1%
3 1
 
< 0.1%
4 50
 
0.1%
5 2126
2.1%
6 58
 
0.1%
7 123
 
0.1%
8 32
 
< 0.1%
9 21
 
< 0.1%
ValueCountFrequency (%)
454 1
 
< 0.1%
453 128
 
0.1%
452 51
 
0.1%
451 1
 
< 0.1%
450 4
 
< 0.1%
449 17
 
< 0.1%
448 9
 
< 0.1%
447 12
 
< 0.1%
446 42
 
< 0.1%
445 499
0.5%

paisOrigen_encoded
Real number (ℝ)

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.0076
Minimum0
Maximum27
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-03-14T21:01:32.529703image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q110
median10
Q310
95-th percentile10
Maximum27
Range27
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.45418543
Coefficient of variation (CV)0.045384049
Kurtosis1027.2833
Mean10.0076
Median Absolute Deviation (MAD)0
Skewness28.658203
Sum994115
Variance0.20628441
MonotonicityNot monotonic
2024-03-14T21:01:32.732704image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
10 99111
99.8%
6 79
 
0.1%
26 57
 
0.1%
8 32
 
< 0.1%
20 15
 
< 0.1%
5 5
 
< 0.1%
9 5
 
< 0.1%
2 4
 
< 0.1%
4 4
 
< 0.1%
25 3
 
< 0.1%
Other values (18) 21
 
< 0.1%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 1
 
< 0.1%
2 4
 
< 0.1%
3 1
 
< 0.1%
4 4
 
< 0.1%
5 5
 
< 0.1%
6 79
0.1%
7 1
 
< 0.1%
8 32
< 0.1%
9 5
 
< 0.1%
ValueCountFrequency (%)
27 1
 
< 0.1%
26 57
0.1%
25 3
 
< 0.1%
24 2
 
< 0.1%
23 1
 
< 0.1%
22 2
 
< 0.1%
21 1
 
< 0.1%
20 15
 
< 0.1%
19 1
 
< 0.1%
18 1
 
< 0.1%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2
52002 
1
35266 
0
8004 
3
 
3717
4
 
347

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters99336
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row3
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 52002
52.3%
1 35266
35.5%
0 8004
 
8.1%
3 3717
 
3.7%
4 347
 
0.3%

Length

2024-03-14T21:01:32.946697image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T21:01:33.144705image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
2 52002
52.3%
1 35266
35.5%
0 8004
 
8.1%
3 3717
 
3.7%
4 347
 
0.3%

Most occurring characters

ValueCountFrequency (%)
2 52002
52.3%
1 35266
35.5%
0 8004
 
8.1%
3 3717
 
3.7%
4 347
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 99336
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 52002
52.3%
1 35266
35.5%
0 8004
 
8.1%
3 3717
 
3.7%
4 347
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Common 99336
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 52002
52.3%
1 35266
35.5%
0 8004
 
8.1%
3 3717
 
3.7%
4 347
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 99336
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 52002
52.3%
1 35266
35.5%
0 8004
 
8.1%
3 3717
 
3.7%
4 347
 
0.3%

asesor_encoded
Real number (ℝ)

Distinct60
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.451327
Minimum0
Maximum59
Zeros24
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-03-14T21:01:33.355702image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q19
median26
Q339
95-th percentile48
Maximum59
Range59
Interquartile range (IQR)30

Descriptive statistics

Standard deviation15.589823
Coefficient of variation (CV)0.61253478
Kurtosis-1.1310214
Mean25.451327
Median Absolute Deviation (MAD)16
Skewness-0.0160912
Sum2528233
Variance243.04258
MonotonicityNot monotonic
2024-03-14T21:01:33.601702image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 8818
 
8.9%
3 7644
 
7.7%
31 7476
 
7.5%
25 6157
 
6.2%
45 5782
 
5.8%
30 5586
 
5.6%
4 5436
 
5.5%
26 5035
 
5.1%
8 4992
 
5.0%
39 4590
 
4.6%
Other values (50) 37820
38.1%
ValueCountFrequency (%)
0 24
 
< 0.1%
1 3551
3.6%
2 1
 
< 0.1%
3 7644
7.7%
4 5436
5.5%
5 2
 
< 0.1%
6 21
 
< 0.1%
7 336
 
0.3%
8 4992
5.0%
9 3397
3.4%
ValueCountFrequency (%)
59 566
 
0.6%
58 5
 
< 0.1%
57 15
 
< 0.1%
56 13
 
< 0.1%
55 92
 
0.1%
54 22
 
< 0.1%
53 2176
2.2%
52 6
 
< 0.1%
51 23
 
< 0.1%
50 174
 
0.2%

grupoEconomico_encoded
Real number (ℝ)

Distinct89
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.256745
Minimum0
Maximum88
Zeros2576
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-03-14T21:01:33.893697image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q131
median42
Q343
95-th percentile88
Maximum88
Range88
Interquartile range (IQR)12

Descriptive statistics

Standard deviation20.488598
Coefficient of variation (CV)0.48485983
Kurtosis0.53885422
Mean42.256745
Median Absolute Deviation (MAD)10
Skewness0.55209234
Sum4197616
Variance419.78265
MonotonicityNot monotonic
2024-03-14T21:01:34.133697image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31 17089
17.2%
42 16410
16.5%
36 10853
10.9%
43 9535
9.6%
88 7243
 
7.3%
35 3955
 
4.0%
70 3539
 
3.6%
60 2728
 
2.7%
0 2576
 
2.6%
5 2546
 
2.6%
Other values (79) 22862
23.0%
ValueCountFrequency (%)
0 2576
2.6%
1 115
 
0.1%
2 177
 
0.2%
3 151
 
0.2%
4 1218
1.2%
5 2546
2.6%
6 10
 
< 0.1%
7 257
 
0.3%
8 1156
1.2%
9 60
 
0.1%
ValueCountFrequency (%)
88 7243
7.3%
87 13
 
< 0.1%
86 22
 
< 0.1%
85 180
 
0.2%
84 221
 
0.2%
83 39
 
< 0.1%
82 1557
 
1.6%
81 85
 
0.1%
80 145
 
0.1%
79 157
 
0.2%
Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
4
55768 
0
40177 
3
 
2002
2
 
806
1
 
583

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters99336
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row4
4th row4
5th row0

Common Values

ValueCountFrequency (%)
4 55768
56.1%
0 40177
40.4%
3 2002
 
2.0%
2 806
 
0.8%
1 583
 
0.6%

Length

2024-03-14T21:01:34.349704image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T21:01:34.535702image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
ValueCountFrequency (%)
4 55768
56.1%
0 40177
40.4%
3 2002
 
2.0%
2 806
 
0.8%
1 583
 
0.6%

Most occurring characters

ValueCountFrequency (%)
4 55768
56.1%
0 40177
40.4%
3 2002
 
2.0%
2 806
 
0.8%
1 583
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 99336
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 55768
56.1%
0 40177
40.4%
3 2002
 
2.0%
2 806
 
0.8%
1 583
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 99336
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 55768
56.1%
0 40177
40.4%
3 2002
 
2.0%
2 806
 
0.8%
1 583
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 99336
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 55768
56.1%
0 40177
40.4%
3 2002
 
2.0%
2 806
 
0.8%
1 583
 
0.6%

parroquia_canton_encoded
Real number (ℝ)

Distinct584
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean300.34574
Minimum0
Maximum583
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.1 MiB
2024-03-14T21:01:34.756698image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile34
Q1202
median303
Q3431
95-th percentile546
Maximum583
Range583
Interquartile range (IQR)229

Descriptive statistics

Standard deviation161.61995
Coefficient of variation (CV)0.538113
Kurtosis-1.0361243
Mean300.34574
Median Absolute Deviation (MAD)118
Skewness-0.069870897
Sum29835144
Variance26121.007
MonotonicityNot monotonic
2024-03-14T21:01:35.001718image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 4871
 
4.9%
539 4871
 
4.9%
219 4777
 
4.8%
221 3626
 
3.7%
367 3016
 
3.0%
431 2717
 
2.7%
355 2531
 
2.5%
361 2480
 
2.5%
228 2426
 
2.4%
310 1925
 
1.9%
Other values (574) 66096
66.5%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 133
 
0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
4 3
 
< 0.1%
5 4
 
< 0.1%
6 2
 
< 0.1%
7 176
0.2%
8 33
 
< 0.1%
9 358
0.4%
ValueCountFrequency (%)
583 12
 
< 0.1%
582 2
 
< 0.1%
581 1
 
< 0.1%
580 2
 
< 0.1%
579 5
 
< 0.1%
578 10
 
< 0.1%
577 156
0.2%
576 1
 
< 0.1%
575 48
 
< 0.1%
574 1
 
< 0.1%

Interactions

2024-03-14T21:01:15.083094image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T20:59:49.074264image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T20:59:56.670099image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:01.600101image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:06.748078image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:11.867492image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:16.174009image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:20.878974image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:26.054167image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:30.723253image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:35.575947image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:40.865052image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:45.458726image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:50.571090image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:55.324859image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:00.270034image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:04.837121image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:10.008842image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:15.582091image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T20:59:51.672821image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T20:59:56.916912image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:02.151100image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:07.006076image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:12.098540image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:16.396132image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:21.128980image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:26.289179image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:30.961252image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:35.831941image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:41.112053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:45.707237image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:50.801100image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:55.580850image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:00.524034image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:05.090999image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:10.288854image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:15.851119image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T20:59:51.998820image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T20:59:57.179934image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:02.436099image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:07.272081image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:12.335063image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:16.657137image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:21.450976image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:26.526176image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:31.231848image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:36.087941image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:41.383052image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:45.978242image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:51.088100image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:55.853856image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:00.774036image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:05.346015image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:10.543855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:16.139111image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T20:59:52.541347image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T20:59:57.453957image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:02.725129image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:07.548078image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:12.578140image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:16.901160image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:21.748293image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:26.767204image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:31.498890image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:36.385942image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:41.664053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:46.268294image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:51.334130image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:56.107849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:01.026165image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:05.852034image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:10.832851image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:16.395111image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T20:59:52.781868image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T20:59:57.692963image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:02.980141image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:07.866601image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:12.800246image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:17.117182image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:22.067623image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:26.995199image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:31.735887image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:36.624938image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:41.907079image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:46.514778image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:51.558125image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:56.356849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:01.253837image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:06.091031image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:11.129857image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:16.671113image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T20:59:53.046633image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T20:59:57.935958image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:03.236139image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:08.139128image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:13.030769image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:17.360196image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:22.329621image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:27.240201image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:31.976881image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:36.898939image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:42.151088image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:46.766812image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:51.812145image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:56.890854image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:01.492884image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:06.356049image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:11.394863image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:16.951930image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T20:59:53.307629image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T20:59:58.188954image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:03.484658image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:08.398992image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:13.255318image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:17.603202image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:22.604626image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:27.490205image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:32.214891image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:37.177943image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:42.409111image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:47.012830image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:52.068136image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:57.134856image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:01.746880image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:06.617048image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:11.676860image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:17.241037image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T20:59:53.749776image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T20:59:58.449980image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:03.733999image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:08.683728image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:13.496345image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:17.853196image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:22.901824image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:27.745229image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:32.473890image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:37.459944image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:42.651105image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:47.553833image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:52.323183image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:57.455851image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:02.001878image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:06.874046image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:12.013855image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:17.521033image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T20:59:54.101473image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T20:59:58.698975image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:03.993001image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:08.960795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:13.732339image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:18.110201image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:23.192763image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:27.994224image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:32.751914image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:37.737942image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:42.918103image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:47.906111image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:52.593187image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:57.712872image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:02.264884image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:07.130099image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:12.298856image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:17.812565image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T20:59:54.353476image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T20:59:59.021976image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:04.261533image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:09.312897image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:13.982881image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:18.389661image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:23.496284image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:28.264253image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:33.074912image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:38.255054image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:43.217101image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:48.189103image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:52.860188image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:57.984876image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:02.534983image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:07.389773image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:12.594853image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:18.104562image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T20:59:54.605006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T20:59:59.287974image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:04.527075image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:09.617895image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:14.236878image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:18.642733image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:23.813361image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:28.520795image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:33.418911image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:38.550053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:43.482106image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:48.471725image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:53.122186image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:58.248308image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:02.817985image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:07.720772image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:12.907859image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:18.382076image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T20:59:54.847023image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T20:59:59.544318image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:04.760600image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:09.890434image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:14.463404image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:18.880253image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:24.119356image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:28.753788image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:33.724909image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:38.801053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:43.736108image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:48.716728image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:53.363851image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:58.498340image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:03.047991image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:07.987775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:13.189860image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:18.662074image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T20:59:55.107027image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T20:59:59.820314image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:05.020593image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:10.159959image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:14.706942image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:19.113345image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:24.419886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:29.023812image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:33.974917image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:39.070053image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:44.001103image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:48.972725image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:53.643849image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:58.738035image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:03.309990image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:08.254808image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:13.473853image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:18.910596image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T20:59:55.421043image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:00.099313image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:05.305652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:10.408954image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:14.942937image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:19.414359image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:24.697877image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:29.507885image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:34.253947image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:39.350054image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:44.252109image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:49.260282image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:53.929856image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:58.975009image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:03.579010image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:08.554820image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:13.775857image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:19.181157image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T20:59:55.676044image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:00.427836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:05.562654image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:10.627962image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:15.184942image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:19.630405image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:24.937886image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:29.737905image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:34.498940image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:39.701052image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:44.485106image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:49.518309image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:54.224851image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:59.203010image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:03.816008image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:08.808833image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:14.044860image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:19.445153image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T20:59:55.922043image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:00.731364image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:05.913652image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:10.856545image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:15.421952image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:19.876912image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:25.204955image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:29.969456image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:34.773939image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:39.992056image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:44.733105image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:49.807306image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:54.523862image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:59.462009image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:04.067009image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:09.108840image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:14.298074image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:19.742155image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T20:59:56.174037image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:01.006006image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:06.197661image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:11.118929image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:15.678957image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:20.367976image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:25.487957image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:30.219453image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:35.026947image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:40.293057image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:44.972145image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:50.058352image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:54.783850image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:59.765034image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:04.325010image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:09.398835image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:14.545079image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:20.003157image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T20:59:56.425036image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:01.284105image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:06.450084image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:11.625970image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:15.903499image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:20.612981image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:25.797954image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:30.480977image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:35.270939image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:40.556052image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:45.221142image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:50.309004image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:00:55.048852image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:00.022026image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:04.576145image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:09.724836image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2024-03-14T21:01:14.789095image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2024-03-14T21:01:35.258713image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
codigoOficinaedadingresosegresosactivospasivosdiasMaxMorosidadcantidadPrestamoscantidadInversionesnumeroCuotastasaInteresmontoPrestamoinstruccionprofesion_encodedpaisOrigen_encodedasesor_encodedgrupoEconomico_encodedparroquia_canton_encodedzona_geograficagenerocondicion_maritalmorosorangoPrestamoqueVivienda_encodedtipoProducto_encoded
codigoOficina1.000-0.0530.0780.0700.0130.055-0.002-0.131-0.0440.0210.0160.035-0.019-0.006-0.0040.2790.0090.0660.3420.0260.0300.0260.0680.0460.028
edad-0.0531.0000.0600.0670.3420.0780.0230.2300.132-0.106-0.014-0.0400.203-0.076-0.007-0.0030.002-0.0380.0430.0110.2400.0480.0790.2450.057
ingresos0.0780.0601.0000.8560.5700.5320.0490.2120.0610.2210.0730.440-0.063-0.104-0.0050.0320.0220.0040.0250.0000.0100.0140.0000.0250.133
egresos0.0700.0670.8561.0000.5260.6370.1130.211-0.0050.1970.1780.322-0.008-0.148-0.0050.036-0.0010.0120.0210.0050.0100.0020.0160.0250.142
activos0.0130.3420.5700.5261.0000.531-0.0480.2820.1370.236-0.0420.409-0.039-0.048-0.0020.0000.068-0.0140.0060.0050.0000.0000.0000.0000.051
pasivos0.0550.0780.5320.6370.5311.0000.1310.201-0.0580.2130.0770.279-0.064-0.041-0.0050.0240.0350.0280.0070.0000.0030.0050.0000.0150.061
diasMaxMorosidad-0.0020.0230.0490.113-0.0480.1311.0000.157-0.1860.0370.142-0.0140.115-0.124-0.003-0.041-0.0560.0240.0750.0130.0370.5580.3390.0450.039
cantidadPrestamos-0.1310.2300.2120.2110.2820.2010.1571.0000.1310.054-0.0320.0740.091-0.0760.000-0.0970.015-0.0290.0310.0120.0350.0390.0120.0410.020
cantidadInversiones-0.0440.1320.061-0.0050.137-0.058-0.1860.1311.000-0.120-0.140-0.048-0.0740.0540.0060.0150.052-0.0450.0130.0370.0160.0420.0160.0120.016
numeroCuotas0.021-0.1060.2210.1970.2360.2130.0370.054-0.1201.000-0.0480.750-0.1000.083-0.0060.0060.0990.0120.0520.0560.0320.1140.1750.0740.357
tasaInteres0.016-0.0140.0730.178-0.0420.0770.142-0.032-0.140-0.0481.000-0.0360.194-0.228-0.003-0.004-0.1140.0210.0930.0540.0490.1850.1430.0740.472
montoPrestamo0.035-0.0400.4400.3220.4090.279-0.0140.074-0.0480.750-0.0361.000-0.1000.026-0.0060.0200.0670.0110.0260.0290.0300.0530.0740.0560.194
instruccion-0.0190.203-0.063-0.008-0.039-0.0640.1150.091-0.074-0.1000.194-0.1001.000-0.305-0.006-0.053-0.1340.0350.0660.0860.0930.1150.0300.1290.142
profesion_encoded-0.006-0.076-0.104-0.148-0.048-0.041-0.124-0.0760.0540.083-0.2280.026-0.3051.000-0.0040.0450.334-0.0190.1070.2880.0970.1710.0670.1310.261
paisOrigen_encoded-0.004-0.007-0.005-0.005-0.002-0.005-0.0030.0000.006-0.006-0.003-0.006-0.006-0.0041.000-0.002-0.000-0.0040.0130.0000.0170.0000.0130.0380.010
asesor_encoded0.279-0.0030.0320.0360.0000.024-0.041-0.0970.0150.006-0.0040.020-0.0530.045-0.0021.0000.0440.0220.7240.0440.0790.0800.0910.0530.052
grupoEconomico_encoded0.0090.0020.022-0.0010.0680.035-0.0560.0150.0520.099-0.1140.067-0.1340.334-0.0000.0441.000-0.0160.1090.2000.0770.1070.0610.1050.192
parroquia_canton_encoded0.066-0.0380.0040.012-0.0140.0280.024-0.029-0.0450.0120.0210.0110.035-0.019-0.0040.022-0.0161.0000.2810.0270.0490.0560.0390.0680.048
zona_geografica0.3420.0430.0250.0210.0060.0070.0750.0310.0130.0520.0930.0260.0660.1070.0130.7240.1090.2811.0000.0370.0840.0770.0720.0390.040
genero0.0260.0110.0000.0050.0050.0000.0130.0120.0370.0560.0540.0290.0860.2880.0000.0440.2000.0270.0371.0000.1630.0160.0080.0280.034
condicion_marital0.0300.2400.0100.0100.0000.0030.0370.0350.0160.0320.0490.0300.0930.0970.0170.0790.0770.0490.0840.1631.0000.0610.0530.1950.045
moroso0.0260.0480.0140.0020.0000.0050.5580.0390.0420.1140.1850.0530.1150.1710.0000.0800.1070.0560.0770.0160.0611.0000.3030.0850.102
rangoPrestamo0.0680.0790.0000.0160.0000.0000.3390.0120.0160.1750.1430.0740.0300.0670.0130.0910.0610.0390.0720.0080.0530.3031.0000.0430.080
queVivienda_encoded0.0460.2450.0250.0250.0000.0150.0450.0410.0120.0740.0740.0560.1290.1310.0380.0530.1050.0680.0390.0280.1950.0850.0431.0000.087
tipoProducto_encoded0.0280.0570.1330.1420.0510.0610.0390.0200.0160.3570.4720.1940.1420.2610.0100.0520.1920.0480.0400.0340.0450.1020.0800.0871.000

Missing values

2024-03-14T21:01:20.383173image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T21:01:21.656178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

codigoOficinaedadingresosegresosactivospasivosdiasMaxMorosidadcantidadPrestamoscantidadInversionesnumeroCuotastasaInteresmontoPrestamozona_geograficagenerocondicion_maritalinstruccionmorosorangoPrestamoprofesion_encodedpaisOrigen_encodedqueVivienda_encodedasesor_encodedgrupoEconomico_encodedtipoProducto_encodedparroquia_canton_encoded
numeroCliente
7173131037866.00549.0013500.010973.0027.01.00.024.015.003000.001211007310134230285
67142410421645.00892.0058000.028000.0032.020.00.072.015.2510000.0011220315510147430260
84646912383687.141194.98118700.086279.4768.01.00.060.014.9920000.0021121315410339424173
562325570500.00200.000.00.00116.05.00.030.023.006000.0011131044010225324361
744631612500.00650.00101000.00.0020.03.00.012.012.002000.00112100110226270221
8858351529460.00140.005000.00.000.01.00.048.014.995000.003232001511014200508
772353230651.00350.005100.00.00144.04.00.030.018.504701.861233129510130404223
641603238750.00250.0022795.00.0018.04.07.036.012.508816.001223039510283649
7339211133360.00100.000.00.000.01.00.012.023.301000.0032130029110120354394
6495199733810.002849.00170920.00.000.01.00.060.018.5920000.001123009510248364219
codigoOficinaedadingresosegresosactivospasivosdiasMaxMorosidadcantidadPrestamoscantidadInversionesnumeroCuotastasaInteresmontoPrestamozona_geograficagenerocondicion_maritalinstruccionmorosorangoPrestamoprofesion_encodedpaisOrigen_encodedqueVivienda_encodedasesor_encodedgrupoEconomico_encodedtipoProducto_encodedparroquia_canton_encoded
numeroCliente
66232265515103.521082.0063100.08847.000.01.00.036.023.305000.00111200951023744177
847092332543.30210.005000.00.000.02.00.060.014.505100.00111303154101942050
8266974291572.00409.0041000.03500.003.01.00.033.019.503060.0012320011110129604355
8871999271035.00521.0021700.010187.160.01.00.072.014.2511500.0012430015410248420497
284694702000.00693.0023200.09363.977.010.00.016.017.9015450.001213002010229314355
8166719352602.002381.7176905.033955.6078.04.00.036.014.993038.23121100201024831094
28049454750.84220.0084994.00.0018.012.00.041.019.509000.0011120015410229424355
6960201148700.00415.0056000.09240.00166.03.00.06.022.00500.0031320015410220424539
779299631850.00690.0063000.06200.005.04.011.036.015.509500.0012110023510248360220
6914902511088.00502.7116300.07079.403.01.00.018.015.004650.0011310051003010261